Course Information for Spring 2016

Lab Instructor Information

Office hours: Knock
M 10pm-late, R 7:30pm-late
Any time my door is open

Course Description

Investigates designing computer programs that extract information from
digital images. Major topics include image formation and acquisition,
gray-scale and color image processing, image filters, feature
detection, texture, object segmentation, classification, recognition,
and motion estimation. Students are introduced to classic and
contemporary vision techniques with examples for homework and
programming assignments drawn from biological and medical imaging,
robotics, augmented reality, and digital photography. Students will
develop small and medium-scale vision systems to solve practical
problems and possibly assist in active research projects at Colby.

Course Goals

Students understand the fundamentals of image formation and image
acquisition.

Students understand and can implement image processing routines
used in computer vision algorithms, such as filtering and
morphological operations.

Students can discuss and implement algorithms for feature
detection, segmentation, classification, and tracking.

Students work in a group to design and develop a medium-sized
image analysis and computer vision application.

Students present algorithms and results in an organized and
competent manner, both written and orally.

Textbooks

There are no great computer vision textbooks. There are good computer
vision textbooks that are somewhat old (Stockman and Shapiro, or Sonka
and Hlavac). There is a reasonable computer vision text that is free
in electronic form (Szeliski). There are a number of reference style
texts, mostly covering the software OpenCV and its various language
APIs. You can access a decent OpenCV reference in the Colby Library
Safari Online service. I would recommend downloading
the Szeliski Book and using the OpenCV
reference to get both the theoretical and practical side of computer
vision.